通过人机协作修正模型偏见，让业务流程预测更公平
FairLoop: Software Support for Human-Centric Fairness in Predictive Business Process Monitoring
- 从神经网络提取决策树，让用户审查并修改不公平逻辑
- 在不剔除敏感属性的前提下，实现针对性偏差消除
- 适合需要可解释公平性的企业级预测系统开发者
性别、年龄等敏感属性可能导致机器学习任务（如预测性业务流程监控）产生不公平预测，尤其在未考虑上下文的情况下。我们提出 FairLoop，一个用于神经网络预测模型的人机协同偏差缓解工具。FairLoop 从神经网络中提炼决策树，使用户能够检查并修改不公平的决策逻辑，再以此微调原始模型以实现更公平的预测。相比其他公平性方法，FairLoop 通过人类参与实现上下文感知的偏差消除，选择性地处理敏感属性的影响，而非统一排除。
原文摘要 · Abstract (English)
Sensitive attributes like gender or age can lead to unfair predictions in machine learning tasks such as predictive business process monitoring, particularly when used without considering context. We present FairLoop1, a tool for human-guided bias mitigation in neural network-based prediction models. FairLoop distills decision trees from neural networks, allowing users to inspect and modify unfair decision logic, which is then used to fine-tune the original model towards fairer predictions. Compared to other approaches to fairness, FairLoop enables context-aware bias removal through human involvement, addressing the influence of sensitive attributes selectively rather than excluding them uniformly.
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